The security industry is in need of technological innovation for producing automated image analysis systems for detecting concealed objects underneath a person's clothing. As shown in FIG. 1, existing imaging modalities include a visible image 100, a millimeter wave image (mmW) 102, and backscatter X-ray image 104. The objective in such a system is to automate the process so that a human operator is not required to look at raw data. However, as shown, a visible image 100 does not identify concealed objects. Alternatively, raw scans (such as the mmW 102 and backscatter X-ray 104 images) reveal what is underneath the clothing, which is the naked appearance of subjects. In the current state of technology, human operators have to directly inspect the scans displaying human subjects essentially in a naked form. As such, these systems generate concerns in the public over privacy issues. Another example of such a raw scan is depicted in FIG. 2, which illustrates a frontal image 200 and a rear image 202 of a subject.
In almost all the existing mmW and backscatter X-ray scanning systems, the task of image inspection and analysis of the image content for the presence of concealed objects (e.g., weapons) is carried out manually by human operators. Although some attempts are being made at automating the process by device manufacturers such as Rapiscan Systems Inc. and Trex Enterprises Corporation, no fully automated system currently exists that can scan and reliably analyze two-dimensional (2D) image content for the presence of concealed objects. Rapiscan Systems Inc. is located at 2805 Columbia Street Torrance, Calif. 90503. Trex Enterprises Corporation is located at 10455 Pacific Center Court, San Diego, Calif. 92121.
Additionally, several recent patent applications have been filed that reveal a number of object detection related systems devised within the context of medical imaging. The focus of related prior art on anomaly detection in the context of medical imaging is often on feature based anomalous/cancerous cell detection. For example, U.S. patent application Ser. No. 10/633,815, by Wrigglesworth et al., describes a system for detecting anomalous targets, namely cancerous cells. In this particular paradigm, a predefined number of features are extracted from a particular set of cell imagery. The features are then used to generate a probabilistic belief function to determine a probability that at least some of the cells in an image set are anomalous.
As another example, U.S. Pat. No. 7,072,435, issued to Metz et al., describes a system that processes computer tomography scans for detecting anomalous cancerous cells associated with lung cancer. The novelty in this particular patent is the use of CT imaging modality, while the algorithms for determining anomalies are somewhat standard computer aided detection (classification) methods.
As yet another example, U.S. patent application Ser. No. 10/352,867, by Parker et al., describes a system in which segmentation and localization of biomarkers, such as liver metastases and brain lesions, are cast as an anomaly detection problem. Statistical segmentation methods are used to identify biomarkers in the first image and the process is carried over to several subsequent images that are time evolutions of the biological construct (liver or brain tissue) that is under observation. The method relies on processing temporal sequences of three-dimensional medical imagery (instead of two-dimensional views).
The prior art is limited for these particular types of imagery. The limitation is primarily due to availability and the need for use of such scanning modalities for security purposes in the public domain. Prior art largely remains in the medical imaging domain where the task of anomaly detection is posed as identification of cancerous lesions. In the mmW and backscatter X-ray image analysis domain, device manufacturers have made attempts at bringing together a combination of off-the-shelf algorithms to analyze images for weapon detection, yet a reliable system does not currently exist that is able to detect weapons in such scans. The existing methods are often not tailored to the specific nature of the data.
A literature and patent search on prior art has revealed related work, particularly in the area of medical imaging; however, as indicated herein, the methods used pose significant vulnerabilities to geometric deformations, image appearance variations and partial occlusions. Addressing these issues is important in creating a reliable automated detection system.
Thus, a continuing need exists for a system that uses an analysis and detection technique to automatically identify concealed objects without manual intervention